PP DocBlockLayout Onnx
A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The...
Model source
- Kind
- Model
- File size
- 123 MB
- Version
- vRevision bcbfaad
- Updated
- Oct 30, 2026
- Downloads
- 317
- Source
- Hugging Face
Source description
PP-DocBlockLayout
Introduction
A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 1 category: Region.
| Model | mAP(0.5) (%) |
|---|---|
| PP-DocBlockLayout | 95.9 |
Note: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks.
Model Usage
Install Dependencies
pip install -U paddleocr
pip install -U onnxruntime-gpu
CLI Usage
paddleocr layout_detection -i ./demo.jpg --model_name PP-DocBlockLayout --engine onnxruntime
Python API Usage
from paddleocr import LayoutDetection
model = LayoutDetection(
model_name="PP-DocBlockLayout",
engine="onnxruntime",
)
output = model.predict("./demo.jpg", batch_size=1)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/res.json")
Sources
1 source
Sources
1 sourceVerified Oct 30
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
3 excerpts
Source excerpts
3 excerpts--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - layout_detection --- # PP-DocBlockLayout ## Introduction A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 1 category: Region. | Model| mAP(0.5) (%) | | --- | --- | |PP-DocBlockLayout | 95.9 | **Note**: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks. ## Model Usage ### Install Dependencies ```shell pip install -U paddleocr pip install -U onnxruntime-gpu ``` ### CLI Usage ```shell paddleocr layout_detection -i ./demo.jpg --model_name PP-DocBlockLayout --engine onnxruntime ``` ### Python API Usage ```python from paddleocr import LayoutDetection model = LayoutDetection( model_name="PP-DocBlockLayout", engine="onnxruntime", ) output = model.predict("./demo.jpg", batch_size=1) for res in output: res.print() res.save_to_img(save_path="./output/") res.save_to_json(save_path="./output/res.json") ```
Source context: 317 downloads · 0 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/PP-DocBlockLayout_onnx
Source context: 53 downloads · 0 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/PP-DocBlockLayout_onnx
